An ECG-based machine-learning approach for mortality risk assessment in a large European population
The proposed model showed a good predictive performance in patients without severe ECG abnormalities. Therefore, this study highlights the potential of ECGs as prognostic rather than diagnostic tools.
Authors
- Martina Doneda (ORCID: https://orcid.org/0000-0002-1660-3100)
- Cecilia Vernia (ORCID: https://orcid.org/0000-0002-1866-7968)
- Claudio Giberti (ORCID: https://orcid.org/0000-0002-5788-3903)
- Ettore Lanzarone (ORCID: https://orcid.org/0000-0001-8816-9086)
- Federico Silipo (ORCID: https://orcid.org/0000-0003-3596-3766)
- Paolo Giovanardi (ORCID: https://orcid.org/0000-0001-7321-5262)
- Andi Vjerdha
Institutions
- University of Bergamo (IT)
- University of Modena and Reggio Emilia (IT)
- Azienda Unita' Sanitaria Locale Di Modena (IT)
- Azienda Ospedaliero-Universitaria di Modena (IT)
- Politecnico di Milano (IT)
Publication Details
- Journal
- Journal of Electrocardiology
- Published
- 2024-11-29
- DOI
- https://doi.org/10.1016/j.jelectrocard.2024.153850
- Citations
- 4
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.61
Funders
- European Commission